Xuecheng Shao

Research Interests

Development of electronic structure methods and software for large-scale ab-initio materials simulations.
Development of complex crystal structure prediction methods and software.
Materials simulation based on density functional theory.
Investigating the physical and chemical properties of materials.

news

Dec 14, 2023	Machine learning electronic structure methods paper published.
Oct 07, 2023	DFTpy version 2.1.0 has been released.
Sep 20, 2023	QEpy version 6.5.0 has been released.

selected publications

Machine Learning Electronic Structure Methods Based On The One-Electron Reduced Density Matrix

Xuecheng Shao^†, Lukas Paetow, Mark E Tuckerman^†, and Michele Pavanello^†

Nat. Commun. 14, 6281 (2023)

Bib HTML PDF Code

@article{shao2023machine,
  author = {Shao, Xuecheng and Paetow, Lukas and Tuckerman, Mark E and Pavanello, Michele},
  journal = {Nature Communications},
  title = {{Machine Learning Electronic Structure Methods Based On The One-Electron Reduced Density Matrix}},
  year = {2023},
  issn = {2041-1723},
  number = {6281},
  pages = {6281},
  volume = {14},
  doi = {10.1038/s41467-023-41953-9},
}

A symmetry-orientated divide-and-conquer method for crystal structure prediction

Xuecheng Shao, Jian Lv, Peng Liu, Sen Shao, Pengyue Gao, Hanyu Liu, Yanchao Wang, and Yanming Ma

J. Chem. Phys. 156, 014105 (2022)

Bib HTML PDF Code

@article{shao2022symmetry,
  author = {Shao, Xuecheng and Lv, Jian and Liu, Peng and Shao, Sen and Gao, Pengyue and Liu, Hanyu and Wang, Yanchao and Ma, Yanming},
  journal = {The Journal of Chemical Physics},
  title = {{A symmetry-orientated divide-and-conquer method for crystal structure prediction}},
  year = {2022},
  number = {1},
  pages = {014105},
  volume = {156},
  publisher = {AIP Publishing LLC},
  doi = {10.1063/5.0074677},
}

DFTpy: An efficient and object-oriented platform for orbital-free DFT simulations

Xuecheng Shao, Kaili Jiang, Wenhui Mi, Alessandro Genova, and Michele Pavanello

WIREs Comput. Mol. Sci. 11, e1482 (2021)

Bib HTML PDF Code

@article{shao2021dftpy,
  author = {Shao, Xuecheng and Jiang, Kaili and Mi, Wenhui and Genova, Alessandro and Pavanello, Michele},
  journal = {Wiley Interdisciplinary Reviews: Computational Molecular Science},
  title = {{DFTpy: An efficient and object-oriented platform for orbital-free DFT simulations}},
  year = {2021},
  number = {1},
  pages = {e1482},
  volume = {11},
  publisher = {Wiley Periodicals, Inc. Hoboken, USA},
  doi = {10.1002/wcms.1482},
}

Efficient DFT Solver for Nanoscale Simulations and Beyond

Xuecheng Shao, Wenhui Mi, and Michele Pavanello

J. Phys. Chem. Lett. 12, 4134 (2021)

Bib HTML PDF

@article{shao2021efficient,
  author = {Shao, Xuecheng and Mi, Wenhui and Pavanello, Michele},
  journal = {The Journal of Physical Chemistry Letters},
  title = {{Efficient DFT Solver for Nanoscale Simulations and Beyond}},
  year = {2021},
  number = {17},
  pages = {4134--4139},
  volume = {12},
  publisher = {ACS Publications},
  doi = {10.1021/acs.jpclett.1c00716},
}

Revised Huang-Carter nonlocal kinetic energy functional for semiconductors and their surfaces

Xuecheng Shao, Wenhui Mi, and Michele Pavanello

Phys. Rev. B 104, 045118 (2021)

Bib HTML PDF

@article{shao2021revised,
  author = {Shao, Xuecheng and Mi, Wenhui and Pavanello, Michele},
  journal = {Physical Review B},
  title = {{Revised Huang-Carter nonlocal kinetic energy functional for semiconductors and their surfaces}},
  year = {2021},
  number = {4},
  pages = {045118},
  volume = {104},
  publisher = {APS},
  doi = {10.1103/PhysRevB.104.045118},
}

Large-scale ab initio simulations for periodic system

Xuecheng Shao, Qiang Xu, Sheng Wang, Jian Lv, Yanchao Wang, and Yanming Ma

Comput. Phys. Commun. 233, 78 (2018)

Bib HTML PDF Code

@article{shao2018large,
  author = {Shao, Xuecheng and Xu, Qiang and Wang, Sheng and Lv, Jian and Wang, Yanchao and Ma, Yanming},
  journal = {Computer Physics Communications},
  title = {{Large-scale ab initio simulations for periodic system}},
  year = {2018},
  pages = {78--83},
  volume = {233},
  publisher = {Elsevier},
  doi = {10.1016/j.cpc.2018.07.009},
}